Introduction to Bayesian Econometrics by Edward Greenberg (English) Hardcover Bo

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This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized.

FORMATHardcover LANGUAGEEnglish CONDITIONBrand New Publisher Description

This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.

Author Biography

Edward Greenberg is Professor Emeritus of Economics at Washington University, St Louis, where he served as a Full Professor on the faculty from 1969 to 2005. Professor Greenberg also taught at the University of Wisconsin, Madison, and has been a Visiting Professor at the University of Warwick (UK), Technion University (Israel) and the University of Bergamo (Italy). A former holder of a Ford Foundation Faculty Fellowship, Greenberg is the author of the first edition of Introduction to Bayesian Econometrics (Cambridge University Press, 2008) and the co-author of four books: Wages, Regime Switching, and Cycles (1992), The Labor Market and Business Cycle Theories (1989), Advanced Econometrics (1983, revised 1991) and Regulation, Market Prices, and Process Innovation (1979). His published research has appeared in leading journals such as the American Economic Review, Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, Biometrika and the Journal of Economic Behavior and Organization. Professor Greenberg's current research interests include dynamic macroeconomics as well as Bayesian econometrics.

Table of Contents

Part I. Fundamentals of Bayesian Inference: 1. Introduction; 2. Basic concepts of probability and inference; 3. Posterior distributions and inference; 4. Prior distributions; Part II. Simulation: 5. Classical simulation; 6. Basics of Markov chains; 7. Simulation by MCMC methods; Part III. Applications: 8. Linear regression and extensions; 9. Semiparametric regression; 10. Multivariate responses; 11. Time series; 12. Endogenous covariates and sample selection; A. Probability distributions and matrix theorems; B. Computer programs for MCMC calculations.

Review

'Edward Greenberg's Introduction to Bayesian Econometrics provides clear and concise coverage of Bayesian theory, computational methods, and important applications. Three years of teaching from its first edition convince me that it is a splendid textbook. The second edition is further enhanced by more applications and new guidance on use of free R software.' John P. Burkett, University of Rhode Island'The apple has not fallen far from the tree, as this second edition of Introduction to Bayesian Econometrics continues in the fine tradition of its predecessor. Along with considerable new material, this second edition contains a thoughtful discussion of important models in time series and financial econometrics (including ARCH/GARCH and stochastic volatility models), as well as an introduction to flexible Bayesian techniques for distribution and regression function modeling. Throughout the text Greenberg engages the reader with an accessible writing style, real data applications, and references to the R programming language. There is much to be learned within these pages. Students and researchers in statistics, biostatistics, economics, and the social sciences will find this to be a tremendously valuable resource.' Justin Tobias, Purdue UniversityReview of the first edition: 'Professor Greenberg has assembled a tremendously valuable resource for anyone who wants to learn more about the Bayesian world. The book begins at an introductory level that should be accessible to a wide range of readers and then builds on these fundamental ideas to help the reader develop an in-depth understanding of modern Bayesian econometrics. The explanations are very clearly written, and the content is supported with many detailed examples and real-data applications.' Douglas J. Miller, University of Missouri, ColumbiaReview of the first edition: 'This concise textbook covers the theoretical underpinnings of econometrics, the MCMC algorithm, and a large number of important econometric applications in an accessible yet rigorous manner. I highly recommend Greenberg's book as a PhD-level textbook and as a source of reference for researchers entering the field.' Rainer Winkelmann, University of ZurichReview of the first edition: 'This book provides an excellent introduction to Bayesian econometrics and statistics with many references to the recent literature that will be very helpful for students and others who have a strong background in calculus.' Arnold Zellner, University of Chicago

Promotional

This textbook is an introduction to econometrics from the Bayesian viewpoint. The second edition includes new material.

Review Quote

'The apple has not fallen far from the tree, as this second edition of Introduction to Bayesian Econometrics continues in the fine tradition of its predecessor. Along with considerable new material, this second edition contains a thoughtful discussion of important models in time series and financial econometrics (including ARCH/GARCH and stochastic volatility models), as well as an introduction to flexible Bayesian techniques for distribution and regression function modeling. Throughout the text Greenberg engages the reader with an accessible writing style, real data applications, and references to the R programming language. There is much to be learned within these pages. Students and researchers in statistics, biostatistics, economics, and the social sciences will find this to be a tremendously valuable resource.' Justin Tobias, Purdue University

Promotional "Headline"

This textbook is an introduction to econometrics from the Bayesian viewpoint. The second edition includes new material.

Description for Bookstore

This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized.

Description for Library

This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized.

Details ISBN1107015316 Author Edward Greenberg Publisher Cambridge University Press Year 2012 Edition 2nd ISBN-10 1107015316 ISBN-13 9781107015319 Format Hardcover Imprint Cambridge University Press Place of Publication Cambridge Country of Publication United Kingdom Replaces 9780521858717 DEWEY 330.01519542 Media Book Short Title INTRO TO BAYESIAN ECONOMETRICS Language English Publication Date 2012-11-12 Pages 270 Affiliation Washington University, St Louis UK Release Date 2012-11-12 AU Release Date 2012-11-12 NZ Release Date 2012-11-12 Illustrations 19 Tables, unspecified; 29 Line drawings, unspecified Edition Description 2nd Revised edition Alternative 9781139058414 Audience Professional & Vocational We've got this

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